

'''simple docstring'''





# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()





class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    StableDiffusionControlNetImgaImgPipeline
				lowerCamelCase_		       =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
				lowerCamelCase_		       =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowerCamelCase_		       =    IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}      )
				lowerCamelCase_		       =    IMAGE_TO_IMAGE_IMAGE_PARAMS







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										torch.manual_seed(0   )
										lowercase :							int    		=UNetaDConditionModel(
										    block_out_channels=(32, 64)		,					layers_per_block=2		,					sample_size=32		,					in_channels=4		,					out_channels=4		,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,					cross_attention_dim=32		,					)
										torch.manual_seed(0   )
										lowercase :							Any    		=ControlNetModel(
										    block_out_channels=(32, 64)		,					layers_per_block=2		,					in_channels=4		,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,					cross_attention_dim=32		,					conditioning_embedding_out_channels=(16, 32)		,					)
										torch.manual_seed(0   )
										lowercase :							Optional[Any]    		=DDIMScheduler(
										    beta_start=0.0_00_85		,					beta_end=0.0_12		,					beta_schedule='''scaled_linear'''		,					clip_sample=UpperCAmelCase__		,					set_alpha_to_one=UpperCAmelCase__		,					)
										torch.manual_seed(0   )
										lowercase :							str    		=AutoencoderKL(
										    block_out_channels=[32, 64]		,					in_channels=3		,					out_channels=3		,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,					latent_channels=4		,					)
										torch.manual_seed(0   )
										lowercase :							Union[str, Any]    		=CLIPTextConfig(
										    bos_token_id=0		,					eos_token_id=2		,					hidden_size=32		,					intermediate_size=37		,					layer_norm_eps=1E-05		,					num_attention_heads=4		,					num_hidden_layers=5		,					pad_token_id=1		,					vocab_size=1000		,					)
										lowercase :							Tuple    		=CLIPTextModel(UpperCAmelCase__   )
										lowercase :							Optional[int]    		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

										lowercase :							List[Any]    		={
										    '''unet''': unet,
										    '''controlnet''': controlnet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components







				def  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Dict=0   ):
										'''simple docstring'''
										if str(UpperCAmelCase__   ).startswith('''mps'''   ):
																lowercase :							List[str]    		=torch.manual_seed(UpperCAmelCase__   )
										else:
																lowercase :							List[str]    		=torch.Generator(device=UpperCAmelCase__   ).manual_seed(UpperCAmelCase__   )

										lowercase :							Optional[Any]    		=2
										lowercase :							Optional[int]    		=randn_tensor(
										    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,					generator=UpperCAmelCase__		,					device=torch.device(UpperCAmelCase__   )		,					)
										lowercase :							int    		=floats_tensor(control_image.shape		,					rng=random.Random(UpperCAmelCase__   )   ).to(UpperCAmelCase__   )
										lowercase :							Dict    		=image.cpu().permute(0		,					2		,					3		,					1   )[0]
										lowercase :							Tuple    		=Image.fromarray(np.uinta(UpperCAmelCase__   )   ).convert('''RGB'''   ).resize((64, 64)   )
										lowercase :							Optional[int]    		={
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 6.0,
										    '''output_type''': '''numpy''',
										    '''image''': image,
										    '''control_image''': control_image,
										}

										return inputs







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3   )







				@unittest.skipIf(
				    torch_device != '''cuda''' or not is_xformers_available()		,					reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,					)
				def  lowerCamelCase_	(       self     :		Any   ):
										'''simple docstring'''
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3   )







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										self._test_inference_batch_single_identical(expected_max_diff=2E-3   )





class 		__SCREAMING_SNAKE_CASE							(			lowercase__						,							lowercase__						,							unittest.TestCase      ):
				lowerCamelCase_		       =    StableDiffusionControlNetImgaImgPipeline
				lowerCamelCase_		       =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
				lowerCamelCase_		       =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowerCamelCase_		       =    frozenset([]      )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										torch.manual_seed(0   )
										lowercase :							Optional[int]    		=UNetaDConditionModel(
										    block_out_channels=(32, 64)		,					layers_per_block=2		,					sample_size=32		,					in_channels=4		,					out_channels=4		,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,					cross_attention_dim=32		,					)
										torch.manual_seed(0   )

										def init_weights(UpperCAmelCase__     :		Optional[Any]   ):
																if isinstance(UpperCAmelCase__		,					torch.nn.Convad   ):
																						torch.nn.init.normal(m.weight   )
																						m.bias.data.fill_(1.0   )

										lowercase :							Optional[int]    		=ControlNetModel(
										    block_out_channels=(32, 64)		,					layers_per_block=2		,					in_channels=4		,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,					cross_attention_dim=32		,					conditioning_embedding_out_channels=(16, 32)		,					)
										controlneta.controlnet_down_blocks.apply(UpperCAmelCase__   )

										torch.manual_seed(0   )
										lowercase :							Dict    		=ControlNetModel(
										    block_out_channels=(32, 64)		,					layers_per_block=2		,					in_channels=4		,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,					cross_attention_dim=32		,					conditioning_embedding_out_channels=(16, 32)		,					)
										controlneta.controlnet_down_blocks.apply(UpperCAmelCase__   )

										torch.manual_seed(0   )
										lowercase :							Optional[int]    		=DDIMScheduler(
										    beta_start=0.0_00_85		,					beta_end=0.0_12		,					beta_schedule='''scaled_linear'''		,					clip_sample=UpperCAmelCase__		,					set_alpha_to_one=UpperCAmelCase__		,					)
										torch.manual_seed(0   )
										lowercase :							List[str]    		=AutoencoderKL(
										    block_out_channels=[32, 64]		,					in_channels=3		,					out_channels=3		,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,					latent_channels=4		,					)
										torch.manual_seed(0   )
										lowercase :							int    		=CLIPTextConfig(
										    bos_token_id=0		,					eos_token_id=2		,					hidden_size=32		,					intermediate_size=37		,					layer_norm_eps=1E-05		,					num_attention_heads=4		,					num_hidden_layers=5		,					pad_token_id=1		,					vocab_size=1000		,					)
										lowercase :							Tuple    		=CLIPTextModel(UpperCAmelCase__   )
										lowercase :							Optional[int]    		=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

										lowercase :							str    		=MultiControlNetModel([controlneta, controlneta]   )

										lowercase :							Tuple    		={
										    '''unet''': unet,
										    '''controlnet''': controlnet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components







				def  lowerCamelCase_	(       self     :		Optional[int]		,					UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		Dict=0   ):
										'''simple docstring'''
										if str(UpperCAmelCase__   ).startswith('''mps'''   ):
																lowercase :							Optional[int]    		=torch.manual_seed(UpperCAmelCase__   )
										else:
																lowercase :							Optional[Any]    		=torch.Generator(device=UpperCAmelCase__   ).manual_seed(UpperCAmelCase__   )

										lowercase :							List[Any]    		=2

										lowercase :							Dict    		=[
										    randn_tensor(
										        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,					generator=UpperCAmelCase__		,					device=torch.device(UpperCAmelCase__   )		,					),
										    randn_tensor(
										        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,					generator=UpperCAmelCase__		,					device=torch.device(UpperCAmelCase__   )		,					),
										]

										lowercase :							List[str]    		=floats_tensor(control_image[0].shape		,					rng=random.Random(UpperCAmelCase__   )   ).to(UpperCAmelCase__   )
										lowercase :							str    		=image.cpu().permute(0		,					2		,					3		,					1   )[0]
										lowercase :							Optional[Any]    		=Image.fromarray(np.uinta(UpperCAmelCase__   )   ).convert('''RGB'''   ).resize((64, 64)   )
										lowercase :							Any    		={
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 6.0,
										    '''output_type''': '''numpy''',
										    '''image''': image,
										    '''control_image''': control_image,
										}

										return inputs







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										lowercase :							Union[str, Any]    		=self.get_dummy_components()
										lowercase :							Optional[int]    		=self.pipeline_class(**UpperCAmelCase__   )
										pipe.to(UpperCAmelCase__   )

										lowercase :							Dict    		=10.0
										lowercase :							str    		=4

										lowercase :							Dict    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Any    		=steps
										lowercase :							Dict    		=scale
										lowercase :							Optional[Any]    		=pipe(**UpperCAmelCase__   )[0]

										lowercase :							Tuple    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							int    		=steps
										lowercase :							Union[str, Any]    		=scale
										lowercase :							int    		=pipe(**UpperCAmelCase__		,					control_guidance_start=0.1		,					control_guidance_end=0.2   )[0]

										lowercase :							Optional[int]    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							int    		=steps
										lowercase :							str    		=scale
										lowercase :							Tuple    		=pipe(**UpperCAmelCase__		,					control_guidance_start=[0.1, 0.3]		,					control_guidance_end=[0.2, 0.7]   )[0]

										lowercase :							Optional[int]    		=self.get_dummy_inputs(UpperCAmelCase__   )
										lowercase :							Optional[Any]    		=steps
										lowercase :							int    		=scale
										lowercase :							Optional[Any]    		=pipe(**UpperCAmelCase__		,					control_guidance_start=0.4		,					control_guidance_end=[0.5, 0.8]   )[0]

										# make sure that all outputs are different
										assert np.sum(np.abs(output_a - output_a   )   ) > 1E-3
										assert np.sum(np.abs(output_a - output_a   )   ) > 1E-3
										assert np.sum(np.abs(output_a - output_a   )   ) > 1E-3







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3   )







				@unittest.skipIf(
				    torch_device != '''cuda''' or not is_xformers_available()		,					reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,					)
				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3   )







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										self._test_inference_batch_single_identical(expected_max_diff=2E-3   )







				def  lowerCamelCase_	(       self     :		List[Any]   ):
										'''simple docstring'''
										lowercase :							Dict    		=self.get_dummy_components()
										lowercase :							List[str]    		=self.pipeline_class(**UpperCAmelCase__   )
										pipe.to(UpperCAmelCase__   )
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )
										with tempfile.TemporaryDirectory() as tmpdir:
																try:
																						# save_pretrained is not implemented for Multi-ControlNet
																						pipe.save_pretrained(UpperCAmelCase__   )
																except NotImplementedError:
																						pass





@slow
@require_torch_gpu
class 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							Any    		=ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''   )

										lowercase :							List[Any]    		=StableDiffusionControlNetImgaImgPipeline.from_pretrained(
										    '''runwayml/stable-diffusion-v1-5'''		,					safety_checker=UpperCAmelCase__		,					controlnet=UpperCAmelCase__   )
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

										lowercase :							Union[str, Any]    		=torch.Generator(device='''cpu'''   ).manual_seed(0   )
										lowercase :							List[Any]    		='''evil space-punk bird'''
										lowercase :							Any    		=load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''   ).resize((512, 512)   )
										lowercase :							Any    		=load_image(
										    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''   ).resize((512, 512)   )

										lowercase :							Union[str, Any]    		=pipe(
										    UpperCAmelCase__		,					UpperCAmelCase__		,					control_image=UpperCAmelCase__		,					generator=UpperCAmelCase__		,					output_type='''np'''		,					num_inference_steps=50		,					strength=0.6		,					)

										lowercase :							Optional[Any]    		=output.images[0]

										assert image.shape == (512, 512, 3)

										lowercase :							Optional[Any]    		=load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy'''   )

										assert np.abs(expected_image - image   ).max() < 9E-2











"""simple docstring"""




def        a__					(							)		->   int:
			return 1



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else two_pence(x - 2							) + one_pence()



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else five_pence(x - 5							) + two_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else ten_pence(x - 10							) + five_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else twenty_pence(x - 20							) + ten_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else fifty_pence(x - 50							) + twenty_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else one_pound(x - 1_00							) + fifty_pence(snake_case__							)



def        a__					(							snake_case__							)		->   int:
			return 0 if x < 0 else two_pound(x - 2_00							) + one_pound(snake_case__							)



def        a__					(							snake_case__ = 2_00							)		->   int:
			return two_pound(snake_case__							)


if __name__ == "__main__":
					print(solution(int(input().strip())))



